• 대한인간공학회지
• /
• 제20권1호
• /
• pp.45-62
• /
• 2001

This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

• 한국컴퓨터정보학회논문지
• /
• 제25권3호
• /
• pp.109-115
• /
• 2020

본 논문에서는 부분 최소 제곱(PLS) 회귀 모형을 이용한 인공지능(AI) 기술 분석을 제안한다. AI 기술은 이제 우리 사회의 대부분의 영역에 영향을 미치고 있다. 따라서 이 기술에 대한 정확한 이해가 필요하게 된다. AI 기술을 분석하기 위하여 전 세계 특허 데이터베이스로부터 AI 관련 특허 문서를 수집하고 텍스트 마이닝 기법을 사용하여 수집된 특허 문서에서 AI 기술 키워드를 추출한다. 본 연구에서는 추출된 AI 키워드 데이터를 PLS 회귀 모형으로 분석한다. 바이오정보학, 사회과학 및 공학 등 다양한 분야에서 고급 데이터 분석을 위하여 사용되는 PLS 회귀 모형은 부분 최소 제곱 기법을 기반으로 한다. 제안 방법의 성능을 확인하기 위하여 AI 특허 문서를 사용하여 분석 실험을 수행하고 제안하는 연구가 실제 문제에 어떻게 적용될 수 있는지 보여 준다. 본 논문은 AI 기술뿐만 아니라 다른 기술 분야에도 적용 할 수 있다.

• Journal of Biosystems Engineering
• /
• 제29권4호
• /
• pp.335-340
• /
• 2004

This study was conducted to evaluate the internal quality of Korean red ginseng using VIS/NIR transmittance spectra. To classify the internal qualities, partial least squares(PLS) regression was conducted. The main results are as follows: To develop the PLS model, several wave bands were divided and incorporated into the model. Among the bands, the wavelength range of 550-1,020nm, excluded noise signal, showed the best evaluation results. Effect of step size on the performance of quality evaluation showed optimal at 15 steps. In order to enhance the accuracy of quality evaluation, the abnormal spectrum shape was considered first and then the PLS model was applied. Among the 150 samples, 12 samples were evaluated by the spectrum shape. In this study, to develop the optimal PLS regression model, among the 150 samples, 138 samples was used with exception of 12 samples which could evaluate the spectrum shape. The result of quality evaluation was promising as SEC and correlation coefficient were 1.09 and 0.967, respectively, and SEP and correlation coefficient were 1.04 and 0.958, respectively.

• 대한인간공학회지
• /
• 제19권3호
• /
• pp.23-37
• /
• 2000

Usability is one of the most important factors that affect customers' decision to purchase a product. Several studies have been conducted to model the relationship between the product design variables and the product usability. Since there could be hundreds of design variables to be considered in the model, a variable screening method is required. Traditional variable screening methods are based on expert opinions (Expert screening) in most Kansei engineering studies. Suggested in this study are statistical methods for screening important design variables by using the principal component regression(PCR), cluster analysis, and partial least squares(PLS) method. Product variables with high effect (PCR screening and PLS screening) or representative variables (Cluster screening) can be used to model the usability. Proposed variable screening methods are used to model the usability for 36 audio/visual products. The three analysis methods (PCR, Cluster, and PLS) show better model performance than the Expert screening in terms of $R^2$, the number of variables in the model, and PRESS. It is expected that these methods can be used for screening the product design variables efficiently.

• 한국농업기계학회:학술대회논문집
• /
• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.III
• /
• pp.560-568
• /
• 2000

The aims of this research were to investigate the preprocessing effect of spectrum data on prediction performance and to develop a robust model to predict SSC in intact apple. Spectrum data of 320 Fuji apples were measured with the on-line transmittance measurement system at the wavelength range of 550∼1100nm. Preprocess methods adopted for the tests were Savitzky Golay, MSC, SNV, first derivative and OSC. Several combinations of those methods were applied to the raw spectrum data set to investigate the relative effect of each method on the performance of the calibration model. PLS method was used to regress the preprocessed data set and the SSCs of samples, and the cross-validation was to select the optimal number of PLS factors. Smoothing and scattering corection were essential in increasing the prediction performance of PLS regression model and the OSC contributed to reduction of the number of PLS factors. The first derivative resulted in unfavorable effect on the prediction performance. MSC and SNV showed similar effect. A robust calibration model could be developed by the preprocessing combination of Savitzky Golay smoothing, MSC and OSC, which resulted in SEP= 0.507, bias=0.032 and R$^2$=0.8823.

• 약학회지
• /
• 제47권3호
• /
• pp.148-153
• /
• 2003

Skin moisture is an important factor in skin health. Measurement of moisture content can provide diagnostic information on the condition of skin. In this study, a portable near-infrared (NIR) system was newly integrated with a photo diode array detector that has no moving parts, and this system has been successfully applied for the evaluation of human skin moisture. Diffuse reflectance spectra were collected and transformed to absorbance using 1 nm step size over the wavelength range of 1100 nm to 2200 nm. Partial least squares regression (PLSR) was applied to develop a calibration model. For practical use for the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo using the portable NIR system on the basis of the relative water content values of stratum corneum from the conventional capacitance method. The PLS model showed a good correlation. The calibration with the use of PLS model predicted human moisture with a standard error of prediction (SEP) of 3.5 at 1120∼1730 nm range. This study showed the possibility of skin moisture measurement using portable NIR system.

• 한국산학기술학회논문지
• /
• 제15권6호
• /
• pp.3931-3943
• /
• 2014

국내의 산업단지는 노후화와 환경문제 등으로 인하여 점점 더 경쟁력을 상실하고 있는 실정이다. 첨단산업단지로의 재편을 위해 공공 및 환경디자인의 질적 향상과 경쟁력 강화를 위한 방향성 정립이 시의적으로 필요하다. 본 연구는 서울디지털산업단지의 이용자 만족도에 영향을 미치는 계획요인을 PLS 회귀모형을 통해 규명하고자 하였다. 분석결과, 총 32개의 계획요인 중 중요도(VIP)값이 1.0 이상으로 매우 의미 있는 디자인 계획요인이 12개로 나타났으며, 0.9~1.0 사이의 비교적 중요한 계획요인이 8개로 도출되었다. 이들 계획요인은 디자인개선사업 시행 시 정량적 우선순위를 가늠할 수 있는 전략적 디자인 계획요인으로 판단할 수 있으며, 서울디지털산업단지의 경쟁력 강화에도 도움이 될 것으로 사료된다.

• Archives of Pharmacal Research
• /
• 제28권4호
• /
• pp.458-462
• /
• 2005

The water content of skin was determined using a FT near infrared (NIR) spectrometer. NIR diffuse reflectance spectra were collected from hairless mouse, in vitro, and from human inner arm, in vivo. It was found that the variation of NIR absorbance band 1450 nm from OH vibration of water and 1940 nm from the combination involving OH stretching and OH deformation, depending on the absolute water content of separated hairless mouse skin, in vitro, using the FT NIR spectrometer. Partial least squares regression (PLSR) was applied to develop a calibration model. The PLS model showed good correlation. For practical use of the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo on the basis of the relative water content of stratum corneum from the conventional capacitance method. The PLS model predicted human skin moisture with a standard errors of prediction (SEP) of 3.98 at 1130-1830 nm range. These studies showed the possibility of a rapid and nondestructive skin moisture measurement using FT NIR spectrometer.

• Journal of Biosystems Engineering
• /
• 제23권1호
• /
• pp.49-56
• /
• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

• Computers and Concrete
• /
• 제13권2호
• /
• pp.173-185
• /
• 2014

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.